Scalable Video Conferencing Using SDN Principles

2025-03-14

AI Summary

The surge in video conferencing traffic has exposed throughput bottlenecks and scalability limitations in traditional Selective Forwarding Units (SFUs). To address this, we propose the first Software-Defined Networking (SDN)-inspired separation architecture for WebRTC: it offloads latency-sensitive, high-throughput media forwarding and packet-loss decision-making to a programmable hardware data plane (built on Barefoot Tofino), while delegating infrequent control-plane tasks—such as feedback analysis—to a lightweight software control plane, thereby decoupling control and data planes. This design is the first to systematically apply the SDN paradigm to SFU infrastructure and remains fully compatible with the standard WebRTC protocol stack. Experimental evaluation demonstrates that our system achieves 7×–210× higher throughput and reduces end-to-end forwarding latency by 26× compared to a 32-core general-purpose server, while supporting production-grade functionality.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Measurement, analysis, and circumvention of Web censorship
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Video-conferencing applications face an unwavering surge in traffic, stressing their underlying infrastructure in unprecedented ways. This paper rethinks the key building block for conferencing infrastructures -- selective forwarding units (SFUs). SFUs relay and adapt media streams between participants and, today, run in software on general-purpose servers. Our main insight, discerned from dissecting the operation of production SFU servers, is that SFUs largely mimic traditional packet-processing operations such as dropping and forwarding. Guided by this, we present Scallop, an SDN-inspired SFU that decouples video-conferencing applications into a hardware-based data plane for latency-sensitive and frequent media operations, and a software control plane for the (infrequent) remaining tasks, such as analyzing feedback signals. Our Tofino-based implementation fully supports WebRTC and delivers 7-210 times improved scaling over a 32-core commodity server, while reaping performance improvements by cutting forwarding-induced latency by 26 times.

Problem

• Addresses scalability issues in video-conferencing infrastructure
• Proposes SDN-inspired SFU for efficient media stream handling
• Reduces latency and improves performance in video-conferencing applications

Innovation

• SDN-inspired SFU decouples hardware and software
• Hardware data plane for latency-sensitive operations
• Tofino-based implementation enhances WebRTC scalability